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Background: The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that schizophrenia patients have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms.
Methods: Here, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. By analyzing an EEG dataset comprising 146 schizophrenia patients and 96 age-matched healthy controls, during resting states and a visual backward masking task.
Results: We found that schizophrenia patients have stronger top-down alpha-band traveling waves compared to healthy controls during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in schizophrenia patients during a visual task, in line with the notion of enhanced signaling of sensory precision errors.
Conclusions: Our results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. Altogether, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.
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| http://dx.doi.org/10.1016/j.biopsych.2024.11.014 | DOI Listing |
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